cellular heterogeneity during mouse pancreatic …gel bead in emulsion (gem) was incubated and then...

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Cellular heterogeneity during mouse pancreatic ductal adenocarcinoma progression at 1

single-cell resolution 2

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Abdel Nasser Hosein1,3,7, Huocong Huang1, Zhaoning Wang2, Kamalpreet Parmar5, Wenting 4

Du1, Jonathan Huang7, Anirban Maitra6,7, Eric Olson2, Udit Verma3, Rolf A. Brekken1,4 5

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1Hamon Center for Therapeutic Oncology Research, Division of Surgical Oncology, Department 7

of Surgery, 2Department of Molecular Biology, 3Department of Internal Medicine – Division of 8

Hematology & Oncology, 4Department of Pharmacology, and 5Department of Pathology, 9

University of Texas Southwestern Medical Center, Dallas, Texas, USA. 6Department of 10

Pathology and 7Department of Translational Molecular Pathology, University of Texas MD 11

Anderson Cancer Center, Houston, TX, USA. 12

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Short title: scRNA-seq of PDA GEMMs 14

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Corresponding author 16

Rolf A. Brekken, PhD 17

Hamon Center for Therapeutic Oncology Research 18

University of Texas Southwestern Medical Center 19

6000 Harry Hines Blvd. 20

Dallas, TX 75390-8593 21

Tel: 214.648.5151; Fax: 214.648.4940 22

[email protected] 23

24

Disclosures 25

The authors have no conflicts of interest to report. 26

27

Writing assistance 28

Editorial assistance was provided by Dave Primm of the UT Southwestern Department of 29

Surgery. 30

31

Funding 32

This work was supported by NIH grants R01 CA192381 and U54 CA210181 Project 2 to RAB, 33

the Effie Marie Cain Fellowship to RAB, the H. Ray and Paula Calvert Pancreatic Cancer 34

Research Fund to UV, NIH grants U01 CA200468, U01 CA196403, R01 CA218004, R01 35

CA204969, P01 CA117696 to AM and grants from the NIH and Welch Foundation to EO. 36

All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.. https://doi.org/10.1101/539874doi: bioRxiv preprint

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Author contributions 37

38

ANH: study concept and design, acquisition of data, analysis and interpretation of data, drafting 39

of the manuscript. HH: study concept and design, acquisition of data, analysis and interpretation 40

of data, drafting of the manuscript. ZW: bioinformatics analyses, interpretation of data. KP: 41

pathologic interpretation and scoring of human tissue samples. WD: acquisition of data. JH: 42

bioinformatics analyses, critical review of the manuscript. AM: bioinformatics analyses, 43

interpretation of data, critical review of the manuscript. EO: bioinformatics analyses, 44

interpretation of data, critical revision of the manuscript. UV: study concept and design, critical 45

revision of the manuscript. RAB: study concept and design, interpretation of data, drafting of 46

the manuscript. 47

48

Abbreviations 49

ADM, acinar-to-ductal metaplasia 50

BET, bromodomain and extraterminal 51

CAF, cancer-associated fibroblast 52

FB, fibroblast population 53

GEM, gel bead in emulsion 54

GEMM, genetically engineered mouse model 55

GO, gene ontology 56

PDA, pancreatic ductal adenocarcinoma 57

PSC, pancreatic stellate cell 58

scRNA-seq, single-cell RNA sequencing 59

UMI, unique molecular identifiers 60

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Abstract: 62

Background & Aims 63

Pancreatic ductal adenocarcinoma (PDA) is a major cause of cancer-related death with limited 64

therapeutic options available. This highlights the need for improved understanding of the biology 65

of PDA progression. The progression of PDA is a highly complex and dynamic process featuring 66

changes in cancer cells and stromal cells; however, a comprehensive characterization of PDA 67

cancer cell and stromal cell heterogeneity during disease progression is lacking. In this study, 68

we aimed to profile cell populations and understand their phenotypic changes during PDA 69

progression. 70

71

Methods 72

We employed single-cell RNA sequencing technology to agnostically profile cell heterogeneity 73

during different stages of PDA progression in genetically engineered mouse models. 74

75

Results 76

Our data indicate that an epithelial-to-mesenchymal transition of cancer cells accompanies 77

tumor progression. We also found distinct populations of macrophages with increasing 78

inflammatory features during PDA progression. In addition, we noted the existence of three 79

distinct molecular subtypes of fibroblasts in the normal mouse pancreas, which ultimately gave 80

rise to two distinct populations of fibroblasts in advanced PDA, supporting recent reports on 81

intratumoral fibroblast heterogeneity. Our data also suggest that cancer cells and fibroblasts are 82

dynamically regulated by epigenetic mechanisms. 83

84

Conclusion 85

This study systematically outlines the landscape of cellular heterogeneity during the progression 86

of PDA. It strongly improves our understanding of the PDA biology and has the potential to aid 87

in the development of therapeutic strategies against specific cell populations of the disease. 88

89

Key words: single-cell RNA sequencing; pancreatic cancer; cellular heterogeneity; fibroblasts; 90

macrophages 91

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Introduction 93

Pancreatic ductal adenocarcinoma (PDA) carries the highest mortality rate of all major 94

malignancies in industrialized countries, with a 5-year survival of 8.5%. Patients are faced with 95

limited treatment options that achieve poor durable response rates, highlighting the need for an 96

improved understanding of PDA disease biology [1]. PDA progression is a complex and 97

dynamic process that requires interaction between cancer cells and stromal cells [2]. It is 98

characterized by the formation of a unique microenvironment consisting of heterogeneous 99

stromal cell populations that include fibroblasts, macrophages, lymphocytes, and endothelial 100

cells. These stromal compartments are critical in driving PDA biology [3]. 101

102

The dynamic phenotypic changes in different cell populations during PDA progression is not 103

fully understood. Gene expression profiling of bulk tissues provides a limited picture of the 104

cellular complexity of the heterogeneous cell populations in PDA. In contrast, single-cell RNA 105

sequencing (scRNA-seq) has the potential to enable gene expression profiling at the level of the 106

individual cell [4] and provides a powerful tool to understand the cellular heterogeneity of PDA. 107

We applied scRNA-seq to investigate gene expression changes of cancer cells and stromal 108

cells during PDA progression in genetically engineered mouse models (GEMMs). This unbiased 109

approach provided evidence of considerable intratumoral cellular heterogeneity, including 110

molecular insights into epithelial and mesenchymal populations of cancer cells and distinct 111

molecular subtypes of macrophages and cancer-associated fibroblasts (CAFs). 112

113

Methods 114

Animal studies 115

KIC, KPC and KPfC mice were generated as previously described [5-7]. Mice were sacrificed 116

when they were moribund: 60 days old for the KIC (n = 3, late PDA) and KPfC (n = 1) or 6 117

months old for the KPC (n = 1). The 2 KIC mice were sacrificed at 40 days old (early PDA) and 118

“normal pancreas” mice (n = 2) were sacrificed at 60 days old. In experiments using more than 119

one mouse, tissues were pooled prior to enzymatic digestion. The KPfC mouse had a pure 120

C57BL/6 genetic background and all others had a mixed background (C57BL/6 with FVB). 121

Ultrasound imaging was carried out under general anesthesia with isoflurane. Mice were 122

euthanized by cervical dislocation under anesthesia. AVMA Guidelines for the Euthanasia of 123

Animals were strictly followed. Tissues were either fixed in 10% formalin for 124

immunohistochemistry or enzymatically digested for single-cell analysis. 125

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Tissue digestion 127

A 10x digestion buffer was prepared in PBS: collagenase type I (450 units/ml, Worthington 128

Biochemical, Lakewood, NJ), collagenase type II (150 units/ml, Worthington), collagenase type 129

III (450 units/ml, Worthington), collagenase type IV (450 units/ml, Gibco/Thermo Fisher, 130

Waltham, MA), elastase (0.8 units/ml, Worthington), hyaluronidase (300 units/ml, Sigma-Aldrich, 131

St. Louis, MO), and DNase type I (250 units/ml, Sigma-Aldrich). Tumors and pancreas were 132

enzymatically digested into a single-cell suspension. Briefly, freshly dissected tissue was placed 133

into a 10-cm tissue culture dish and a sterile razor blade was used to cut the tissue into fine 134

pieces. Samples were resuspended in PBS and washed twice by centrifuge at 2000 rpm for 3 135

minutes and added to a 50 ml tube containing 1x digestion buffer containing 1% FBS. The tube 136

was incubated on a shaker at 37°C for 60 minutes. Then 35 ml of PBS was added and cells 137

were washed three times prior to filtering out debris using a 70 μm mesh filter. Single cells were 138

resuspended in 100 μl of PBS in preparation for single-cell library creation. Cell viability was 139

measured by trypan blue. Viability was 80% for the normal pancreas and late KIC samples, 75% 140

for the early KIC and KPfC, and 90% for the KPC. 141

142

Single-cell cDNA library preparation and sequencing 143

Library generation was performed using the 10x Chromium System (10X Genomics Inc., 144

Pleasanton, CA). Single-cell suspensions were washed in 1x PBS (calcium- and magnesium-145

free) containing 0.04% weight/volume bovine serum albumin (400 μg/ml) and brought to a 146

concentration of 200-700 cells/μl. The appropriate volume of cells was loaded with Single Cell 3’ 147

gel beads into a Single Cell A Chip and run on the Chromium Controller. Gel bead in emulsion 148

(GEM) was incubated and then broken. Silane magnetic beads were used to clean up the GEM 149

reaction mixture. Read 1 primer sequence was added during incubation and full-length, 150

barcoded cDNA was amplified by PCR after cleanup. Sample size was checked on an Agilent 151

Tapestation 4200 (Agilent, Santa Clara, CA) using DNAHS 5000 tape and concentration 152

determined by a Qubit 4 Fluorometer (Thermo Fisher) using the DNA HS assay. Samples were 153

enzymatically fragmented and underwent size selection before proceeding to library 154

construction. During library preparation, Read 2 primer sequence, sample index, and both 155

Illumina adapter sequences were added. Samples were cleaned up using AMPure XP beads 156

(Beckman Coulter, Brea, CA) and post-library preparation quality control was performed using 157

DNA 1000 tape on the Agilent Tapestation 4200. The final concentration was ascertained using 158

the Qubit 4 Fluorometer DNA HS assay. Samples were loaded at 1.5 pM and run on the 159


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Illumina NextSeq500 High Output Flowcell (Illumina, San Diego, CA) using V2.5 chemistry. The 160

run configuration was 26 x 98 x 8. 161

162

Bioinformatic analyses 163

We used Cell Ranger version 1.3.1 (10x Genomics) to process raw sequencing data and the R-164

package Seurat version 2.0 [8] for downstream analyses. Cell clusters were identified via the 165

FindClusters function using a resolution of 0.6 for all samples, using a graph-based clustering 166

algorithm implemented in Seurat. Marker genes for each cluster were computed, and 167

expression levels of several known marker genes were examined. Different clusters expressing 168

known marker genes for a given cell type were selected and combined as one for each cell 169

type. Gene ontology and pathway analysis were performed using the DAVID bioinformatics 170

suite, version 6.8 [9]. 171

172

Histological analysis 173

Formalin-fixed tissues were embedded in paraffin and cut in 5 μm sections. Sections were 174

evaluated by H&E and immunohistochemical analysis using antibodies specific for vimentin 175

(5741, Cell Signaling Technology, Danvers, MA), BRD4 (AB128874, Abcam, Cambridge, MA), 176

Sox9 (AB5535, EMD Millipore, Burlington, MA), CDH11 (NBP2-15661, Novus Biologicals, 177

Centennial, CO), and H3K27ac (AB4729, Abcam). Following an initial antigen retrieval with Tris-178

EDTA-glycerol (10%) buffer and inhibition of endogenous peroxidase activity, the slides were 179

incubated with primary antibody overnight at 4°C. Slides were then incubated with horseradish 180

peroxidase or alkaline phosphatase conjugated secondary antibody (Vector Laboratories, 181

Beringame, CA) for 1 hour at 25°C. This was followed by development using the appropriate 182

chromogenic substrate: DAB, Warp Red or Ferangi Blue (Biocare Medical, Pacheco, CA). In the 183

case of multichannel immunohistochemistry, slides were subsequently stripped using a sodium 184

citrate buffer and by boiling at 110°C for 3 minutes. The procedure was then repeated as above 185

using a different-colored chromogen for development. All human PDA samples were provided 186

by the UT Southwestern Tissue Management Shared Resource and their use was approved by 187

the UT Southwestern institutional review board for the purpose of research. All patient samples 188

were de-identified and interpreted by a board-certified pathologist (KP). 189

190

Results 191

Cellular heterogeneity during PDA progression 192


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We sought to determine the composition of single pancreatic cancer cells during progression in 193

GEMMs. Normal mouse pancreas, 40-day-old KIC (KrasLSL-G12D; Cdkn2aflox/flox; Ptf1aCre/+) mouse 194

pancreas, termed “early KIC” (with early neoplastic changes confirmed by ultrasound; 195

Supplementary Fig. 1), and 60-day-old KIC pancreas, termed “late KIC” (Fig. 1A) were freshly 196

isolated and enzymatically digested followed by single-cell cDNA library generation using the 197

10x Genomics platform [10]. Libraries were subsequently sequenced at a depth of more than 198

105 reads per cell. We performed stringent filtering, normalization, and graph-based clustering, 199

which identified distinct cell populations in the normal pancreas and each stage of PDA. 200

201

In the normal mouse pancreas, 2354 cells were sequenced and classified into appropriate cell 202

types based on the gene expression of known markers: acinar cells, islet cells, macrophages, T 203

cells, and B cells, as well as three distinct populations of fibroblasts. Fibroblasts-1, fibroblasts-2, 204

and fibroblasts-3 (Fig. 1B and E) were noted. In the early KIC pancreas (3524 cells sequenced), 205

the emergence of a cancer cell population was observed (9.9% of cells), expressing known PDA 206

markers such as Krt18 and Sox9 [11] (Fig. 1C and F). The acinar cell population was 207

substantially reduced, while there was a marked increase in total macrophages and fibroblasts. 208

Of note, the same three populations of fibroblasts seen in the normal pancreas were identified in 209

the early KIC lesion. Additionally, endothelial cells were observed at this stage. This indicates 210

that the expansion of fibroblasts and macrophages is an early event during PDA development, 211

accompanying tumor initiation. We next characterized the late KIC pancreas (804 cells 212

sequenced) and noted the absence of normal exocrine (acinar) and endocrine (islet) cells (Fig. 213

1D and G). Instead, two distinct populations of cancer cells were present, suggesting 214

phenotypic cancer cell heterogeneity as a late event in the course of the disease. We also 215

observed the presence of only two distinct fibroblast populations, which had a similar 216

percentage in relation to total cells. Noticeably, macrophages became a predominant cell 217

population in the late KIC tumor. Moreover, we observed lymphocytes at this stage. The cellular 218

heterogeneity in cancer cells and stromal cells in the early and late KIC lesions highlighted the 219

dynamic cellular changes that occur during PDA progression. 220

221

Mesenchymal cancer cells emerge in advanced PDA 222

Gene expression analysis of cancer cell epithelial markers (Cdh1, Epcam, Gjb1, and Cldn3) and 223

mesenchymal markers (Cdh2, Cd44, Axl, Vim, and S100a4) revealed that early KIC cancer cell 224

populations assumed an epithelial expression profile (Fig. 2A and C). This is in contrast to tumor 225

cell populations in the late KIC tumors, where we identified two distinct cancer cell populations: 226


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one enriched for epithelial markers and the other, more abundant population, enriched for 227

mesenchymal markers (Fig. 2B and C). These data support that tumor cell epithelial plasticity 228

contributes to cancer cell heterogeneity during the progression of KIC tumors. 229

230

The hierarchical clustering of the top significant genes in each of the three cancer cell 231

populations (epithelial cancer cells in early KIC, epithelial and mesenchymal cancer cell 232

populations in late KIC) was performed (Fig. 2D). In addition, gene clusters from the cancer cell 233

populations were subjected to pathway and gene ontology (GO) analysis. First, we compared 234

cancer cells of the early KIC population to the total cancer cells of the late KIC and found that 235

the most downregulated genes in late KIC cancer cells were associated with normal pancreatic 236

function such as pancreatic secretion, digestion and absorption, and insulin secretion (Fig. 2E 237

and F). Moreover, normal pancreatic acinar genes such as Try4, Try5, Cela2a, Cela3b, Reg2, 238

and Rnase1 were expressed at higher levels in early KIC cancer cells, while late KIC cancer 239

cells expressed a higher level of the pancreatic ductal gene Muc1 (Fig. 2D). This is suggestive 240

of an ongoing acinar-to-ductal metaplasia (ADM) during tumor progression in this GEMM. In 241

contrast, the most upregulated genes in late KIC cancer cells were associated with ribosome, 242

glycolysis/gluconeogenesis, and amino acid biosynthesis, which is highly suggestive of 243

increased translation and metabolically active cancer cells in established KIC tumors. 244

Interestingly, pathways previously reported to be closely associated with the stroma and 245

progression of PDA were also highlighted, such as ECM-receptor interaction [12], TGFβ [13], 246

and hippo signaling pathways [14]. We then compared early KIC cancer cells with the late KIC 247

epithelial cancer cell population to understand the mechanisms that promoted the progression 248

of PDA in the epithelial cancer cell compartment. Interestingly, similar cell functions/signaling 249

pathways were identified by comparing the two epithelial cancer cell populations (Fig. 2G and 250

H). Taken together, these analyses objectively demonstrate an ADM state during the 251

progression of KIC tumors and suggest that stroma-cancer cell interaction promotes the 252

progression of PDA and cancer cell heterogeneity. 253

254

Mesenchymal cancer cells exist in advanced PDA GEMMs with different diverse mutations 255

In addition to KRAS mutations, additional driver events are required for PDA progression [8], 256

with TP53 and INK4A being the second- and third-most commonly mutated genes in human 257

PDA, respectively. As such, we sought to understand the effect of different secondary driver 258

mutations on the phenotypes and heterogeneity of cancer cells. We performed scRNA-seq in 259

another PDA GEMM, KPfC (KrasLSL-G12D; Trp53Flox/Flox; Pdx1Cre/+) (Fig. 3A). Consistent with late 260


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KIC tumors, two distinct cancer cell populations expressing Krt18 and Sox9 were noted in late 261

KPfC (60-day-old) tumors (Fig. 3A and B), one marked by epithelial markers such as Gjb1, Tjb1, 262

Ocln, and Cldn3, while the other was marked by mesenchymal markers such as Vim, Cd44, Axl, 263

S100a4, and Fbln2 (Fig. 3C and D). Epithelial and mesenchymal cancer cell populations in 264

KPfC mice shared many genes in common with the corresponding populations in KIC; however, 265

they also expressed unique gene signatures (Fig. 3F). 266

267

We then compared the total cancer cell gene signatures between late KIC and late KPfC mice 268

by KEGG and Biocarta pathway analysis methods, in an attempt to identify potential differences 269

in cancer cell signaling pathways caused by the different secondary driver mutations. As 270

expected, the p53 signaling pathway was upregulated in the KIC model by comparison to the 271

KPfC model (Fig. 3E). The analyses of late KIC and late KPfC mice suggests that cancer cell 272

heterogeneity is a late-stage tumor event that occurs in the setting of multiple secondary driver 273

mutations. However, under the same oncogenic Kras mutation, different secondary driver 274

mutations can potentially lead to different signaling pathways that drive PDA progression. 275

276

Macrophage heterogeneity during PDA progression 277

We found a marked increase in the size of the macrophage population as PDA progressed from 278

normal pancreas to early KIC and eventually late KIC tumors (Fig. 1B-D). We further 279

characterized the macrophage compartment during PDA progression by subclustering 280

macrophages in early and late KIC tumors, which revealed three transcriptionally distinct 281

macrophage clusters in early KIC and two in late KIC (Fig. 4A and C). 282

283

Macrophage population 1 in early KIC tumors was characterized by the expression of Fn1, 284

Lyz1, Lyz2, Ear1, and Ear2 as well as Cd14 (Fig. 4B). Moreover, these macrophages 285

specifically expressed high levels of the IL1 receptor ligands: Il1a, Il1b, and Il1rn. GO analysis 286

suggested that this macrophage population was involved in healing during inflammation, the 287

regulation of type I and III hypersensitivities, and antigen processing and presentation (Fig. 4E). 288

In contrast, macrophage population 2 was noted to express an abundance of chemokines, 289

including Ccl2, Ccl4, Ccl7, Ccl8, and Ccl12, as well as many complement-associated genes 290

(Fig. 4B). Indeed, leukocyte activation, complement activation, and humoral response genes 291

were the most significantly enriched GO categories in this macrophage population (Fig. 4E). 292

The third macrophage population expressed Ccl17 and Ccr7 and was enriched in ribosomal 293

small-unit biogenesis, translation, and antigen-processing functions (Fig. 4B and E). Importantly, 294


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macrophages in normal mouse pancreas weakly expressed genes found in macrophage 295

population 2 and 3 from early KIC mice, suggesting that the normal pancreas macrophages 296

could be noncommitted macrophages residing in tissue in the normal organ that are induced to 297

adopt a distinct phenotype upon tumor initiation (Fig. 4B). 298

299

The late KIC tumor featured two macrophage subpopulations (Fig. 4C). Macrophage population 300

1 highly expressed genes such as S100a8 and Saa3, which have been shown to be expressed 301

in lipopolysaccharide-treated monocytes [15]. Moreover, numerous chemokines were elevated 302

in this population such as Ccl2, Ccl7, Ccl9, Ccl6, Cxcl3, and Pf4 (Fig. 4D). GO analysis revealed 303

this population is likely associated with Stat3 activation, leukocyte chemotaxis, and response to 304

lipopolysaccharide and inflammatory stimuli (Fig. 4F). These data suggest that macrophage 305

population 1 was inflammatory in nature. Macrophage population 2 of late KIC tumors was rich 306

in MHC-II antigen presentation molecules: Cd74, H2-Aa, H1-Ab1, H2-Dma, H2-Dmb1, H2-307

Dmb2, and H2-Eb1 (Fig. 4D), and GO analysis highlighted antigen presentation and adaptive 308

immune response pathways as being elevated (Fig. 4F). Consistently, in late KPfC tumors, we 309

also observed two distinct populations of macrophages with similar features (Supplementary 310

Fig. 3). Interestingly, we did not observe a macrophage population in late tumors that correlated 311

with macrophage population 1 from the early tumors, suggesting that this population might 312

undergo negative selection or a differentiation into inflammatory and/or MHC-II–rich 313

macrophages during tumor progression. 314

315

We also compared the features of the total macrophage clusters between early and late KIC 316

tumors and observed a substantially enhanced macrophage inflammatory signature as the 317

tumor progressed (Fig. 4G). A wide variety of inflammatory genes increased, including Il1a, Il1b, 318

Il1r2, and Il6. GO analysis of this gene list highlighted leukocyte chemotaxis and inflammatory 319

response functions as increased in advanced KIC tumors (Fig. 4H). These data suggest that 320

PDA progression is characterized by an increase in inflammatory features in macrophages. 321

322

Fibroblast heterogeneity during PDA progression 323

In normal pancreas and early KIC tumors, we had identified three distinct populations of 324

fibroblasts, while in late KIC only two fibroblast populations were noted (Fig. 1B-D). To ascertain 325

the relationship between these fibroblast populations and the dynamics of their phenotypic 326

changes during PDA progression, we projected fibroblasts from the three analyses onto a single 327

tSNE plot and applied a graph-based clustering algorithm (Fig. 5A) which revealed three distinct 328


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molecular subtypes of fibroblasts in the normal pancreas, early KIC tumors, and late KIC 329

tumors. The overlay demonstrates that the normal pancreas and early KIC tumors contained all 330

three fibroblast subtypes while the late KIC contained only two (Fig. 5A), confirming our initial 331

analysis (Fig. 1B-D). Specifically, this analysis demonstrated that fibroblast population 1 (FB1) 332

and fibroblast population 3 (FB3) found in normal and early KIC pancreas were present in the 333

late KIC tumor whereas fibroblast population 2 (FB2) was absent. 334

335

In the normal pancreas, FB1, FB2, and FB3 made up 35.4%, 56.9% and 7.7% of the total 336

fibroblasts, respectively (Supplementary Fig.4A). In early KIC tumors, although the total 337

fibroblasts expanded (Fig. 1C), the ratios of each fibroblast population remained similar. 338

Furthermore, in the late KIC tumors, FB1 and FB3 were present in nearly equal proportions of 339

46.5% and 53.5%, respectively (Supplementary Fig.4A). Each fibroblast population was 340

characterized by distinct marker genes. For example, FB1 markedly expressed Cxcl14, Ptn, and 341

several genes mediating insulin-like growth factor signaling such as Igf1, Igfbp7, and Igfbp4. 342

FB2 specifically expressed Nov, a member of the CCN family of secreted matricellular proteins 343

[16] as well as Pi16, which has been shown to be expressed in fibroblast populations in various 344

tissue types [17], in addition to Ly6a and Ly6c1. FB3 showed distinct expression of mesothelial 345

markers such as Lrrn4, Gpm6a, Nkain4, Lgals7, and Msln [18] in addition to other genes 346

previously shown to be expressed in fibroblasts such as Cav1, Cdh11, and Gas6 [19-21]. 347

348

Hierarchical clustering of the most significant genes for each fibroblast subtype confirmed the 349

persistence of FB1 and FB3 during the progression of PDA (Fig. 5B) and that they exist across 350

different advanced-stage PDA GEMMs (KPC and KPfC), suggesting a consistent cell of origin. 351

Interestingly, the gene expression heatmap also indicated that the FB2 population started to 352

move toward an FB1-like expression profile in early KIC tumors, suggesting FB1 and FB2 might 353

converge into a single CAF population with FB1 features by late invasive disease. Of note, Il6, 354

Ccl2, Ccl7, Cxcl12, and Pdgfra were expressed in FB1 and FB2 in the normal pancreas and 355

early KIC tumors, and showed greater expression in FB1 of late KIC (Fig. 5C). In contrast, the 356

myofibroblast markers Acta2 and Tagln were expressed by a portion of FB3. These data 357

support the presence of previously described, mutually exclusive, inflammatory (FB1) and 358

myofibroblastic (FB3) CAF subtypes [22-24]. Interestingly, FB3 also expressed numerous MHC-359

II–associated genes (Fig. 5C). GO analysis suggested that FB1 was involved in an acute phase 360

response and inflammatory response, FB2 was more associated with physiological functions of 361

fibroblasts, while FB3 had antigen processing and presentation through the MHC-II pathway 362


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and had complement activation functions (Fig. 5D). Furthermore, we analyzed genes that 363

increased in FB1 and FB3 during PDA progression, and found that FB1 showed a progressive 364

increase in the expression of genes associated with inflammatory response and chemotaxis 365

(Fig. 5E and Supplementary Fig. 4B) while FB3 genes displayed increased function on 366

translation during disease progression, possibly due to enhanced antigen processing activity. 367

These data suggest that FB1 is an inflammatory population and the inflammatory feature 368

increases during PDA progression, while FB3 consists of the well-studied myofibroblast 369

population, and displays an enrichment for class 2 MHC genes. 370

371

We also found that some genes essentially exclusive to FB3 in the normal and early KIC 372

pancreas became expressed in FB1 and FB3 populations in late KIC, marking these genes as 373

potential global fibroblast markers in advanced PDA. One such gene was Cdh11 (Fig. 5C). We 374

validated these data by immunohistochemistry. We found in late KIC tumors, stromal staining 375

for αSMA and PDGFRα were nearly mutually exclusive, whereas CDH11 showed uniform 376

staining across all morphologically discernable fibroblasts (Fig. 5F). Taken together, these data 377

provide the first in vivo description of all CAF populations during PDA progression. 378

379

Mesenchymal cancer cells and CAFs show evidence of increased epigenetic regulation and 380

super-enhancer activity in advanced PDA 381

Unique molecular identifiers (UMI) serve to barcode each input mRNA molecule during cDNA 382

library generation, enabling the determination of initial transcript number even after cDNA library 383

amplification [25]. We compared UMI counts across all cell types between early and late KIC 384

tumors (Fig. 6A and B). In early lesions, there was a marked increase in UMI in the beta islet 385

cells (median: 2849, range: 1322-12,857), which might indicate that increased transcriptional 386

activity is a means by which the endocrine requirements of these cells are met. No other cell 387

population in the early KIC tumor displayed this level of UMI. The early KIC cancer cells 388

displayed a relatively low UMI count (median: 1979, range: 1163-7735). In contrast, the 389

mesenchymal cancer cell population in the late KIC tumor displayed a marked increase in total 390

UMI count with a median count of 18,334 and range of 4433-50,061 (Fig. 6C). The epithelial 391

cancer cells in the late KIC also displayed an increased UMI, albeit to a far lesser degree than 392

the mesenchymal cancer cell population (median: 10,368, range: 4940-30,440). 393

394

We reasoned that the increased transcriptional activity may be associated with increased 395

activity of epigenetic regulation as well as super-enhancer [26]. BRD4 belongs to the 396


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bromodomain family of transcriptional regulators and is a key regulator of super-enhancer 397

activity [27]. Prior studies have shown that MYC activity is promoted by super-enhancer activity 398

in PDA [28]. We found that in late KIC and KPfC tumors, Brd4 was expressed highly in epithelial 399

and mesenchymal cancer cells while Myc was expressed mainly in the mesenchymal cancer 400

cell population (Fig. 6C, Supplementary Fig. 6). In addition, several genes encoding high-401

mobility group A proteins (Hmga1, Hmga1-rs, Hmga2) were markedly expressed in late KIC and 402

KPfC mesenchymal cancer cells. HMGA proteins are chromatin-associated proteins that 403

regulate transcriptional activity, including enhancesome formation [29]. Lastly, critical 404

components of the SWI/SNF complex (Smarcb1, Arid1a, Arid2), which are essential in 405

nucleosome remodeling and transcriptional regulation [30], were also expressed highly in 406

epithelial and mesenchymal cancer cells of the late KIC, but not cancer cells in the early KIC 407

lesion. Taken together, these data provide multiple lines of evidence to suggest that the 408

transcript load of a more aggressive mesenchymal cancer cell population is increased relative to 409

cancer cells in early lesions or epithelial cancer cells in advanced PDA. 410

411

Interestingly, we also noted that fibroblasts in late KIC tumors also showed an increased UMI 412

(median: 14,538, range: 4461-37,497). They also displayed an increased expression of super-413

enhancer and other epigenetic transcriptional regulator genes in contrast to fibroblasts from 414

normal mouse pancreas or early KIC pancreas (Fig. 6D). These data are suggestive of 415

increased super-enhancer and transcriptional activity as normal pancreas fibroblasts become 416

CAFs. 417

418

We validated these single-cell RNA expression data using three-color immunohistochemical 419

analysis of late KIC tumors: SOX9 was used as a pan-cancer cell marker, vimentin as a 420

mesenchymal marker, and BRD4 was a surrogate marker for super-enhancer activity. We 421

identified positive co-staining for vimentin and Brd4 in CAFs, positive triple-staining 422

(vimentin+/Sox9+/Brd4+) in mesenchymal cancer cells, and single staining of Sox9 in epithelial 423

cancer cells that localized to more differentiated, duct-like structures in the advanced tumors 424

(Fig. 6E). Next, we performed immunohistochemical analysis on 16 whole tumor human 425

pancreatic cancer sections using an antibody against H3K27ac, a commonly accepted marker 426

of increased gene regulatory element activity [26, 31]. The malignant epithelium and stromal 427

fibroblasts were scored separately. These analyses showed markedly positive 3+/3+ staining in 428

the stromal fibroblasts of all whole tumor sections (Fig. 6F). In 6/16 cancer epithelia the score 429

was 1+ and 10/16 scored 2+, with no samples showing a cancer epithelial scoring of 3+. Taken 430


https://doi.org/10.1101/539874

14

together, these are the first data indicating differential super-enhancer activity in distinct tissue 431

compartments of PDA. 432

433

Discussion 434

We have carried out an scRNA-seq of different stages of the KIC GEMM, in addition to late 435

KPfC and KPC tumors in an effort to agnostically profile the phenotypic changes of cancer and 436

stromal cells during PDA progression. We have established the emergence of a mesenchymal 437

cancer cell population as a late-stage tumor event and have identified novel features of different 438

macrophage and fibroblast populations. This significantly improves our understanding of PDA 439

progression and lays the foundation for the development of novel therapeutic approaches. 440

441

PDA pathogenesis involves metaplasia of normal acinar cells to ductal epithelium, which in turn 442

undergo neoplastic transformation in a KRAS-driven manner [32]. Malignant ductal epithelium 443

may then assume more aggressive, mesenchymal features as the disease progresses. In this 444

study, mesenchymal cancer cell populations were noted in late-stage tumors. Our data support 445

a model in which mesenchymal features of cancer cells are acquired later in the disease 446

process, although others have argued that this can be one of the earliest events in PDA [33]. 447

Mesenchymal cancer cell populations have been studied extensively in pancreatic cancer 448

mouse models and has been shown to be critical to chemotherapeutic resistance while their 449

contribution to metastasis has been more controversial [34, 35]. Mesenchymal cancer cells 450

have previously demonstrated an increased protein anabolism and activation of the 451

endoplasmic reticulum–stress-induced survival pathways in a PDA GEMM [36]. 452

453

Indeed, in the late KIC model, ribosomal pathways were the most significantly upregulated 454

pathways in cancer cells (Fig. 2E-H). It is likely that the demand for increased ribosomal activity 455

stems from high transcriptional activity governed by epigenetic mechanisms in the 456

mesenchymal cancer cells, as we also saw markedly increased UMI counts in this population 457

(Fig. 6B). The bromodomain and extraterminal (BET) family of proteins such as BRD4, which is 458

markedly upregulated in cancer cells and fibroblasts of late-stage PDA (Fig. 6C and D), serve to 459

recruit regulatory complexes to acetylated histones at enhancer sites, resulting in increased 460

transcription [37]. Previously, a combination approach using a BET protein inhibitor and a 461

histone deacetylase inhibitor led to near-complete tumor regression and improved animal 462

survival in a PDA GEMM [28]. Super-enhancer activation has recently been shown to be 463

fundamental in the pathophysiology of a variety of neoplasms [38] and is intimately associated 464


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15

with Hmg2a in PDA, as super-enhancer attenuation has been demonstrated to downregulate 465

Hmg2a expression and the growth of PDA cells in a three-dimensional in vitro model [39]. Our 466

study is the first to demonstrate that these epigenetic regulatory mechanisms in PDA are 467

present in specific tissue compartments, namely mesenchymal cancer cells and CAFs. Future 468

efforts to target super-enhancer activity in PDA should consider distinct tissue compartments 469

governing the sensitivity and resistance to novel therapeutics. 470

471

Our data revealed two molecular subtypes of macrophages in advanced PDA (Fig. 4). One 472

expressed numerous chemokine and inflammation-associated genes while the other was rich in 473

MHC-II–associated genes. In a previous study, MHC-II–positive macrophages were isolated 474

from orthotopic breast tumors and highly expressed CCL17, consistent with our data [40]. In 475

parallel with our study, MHC-IIlow macrophages were found to be highly enriched for numerous 476

chemokines. Moreover, in an orthotopic hepatoma mouse model, an early MHC-II+ macrophage 477

population appeared to suppress tumor growth but an MHC-IIlow macrophage population 478

became the predominant macrophage population as the tumor progressed, resulting in a 479

protumor phenotype [41]. Nonetheless, to confirm their pathophysiological significance, 480

functional studies are required in which inducible selective ablation [42] is performed on the two 481

late-stage PDA macrophage subpopulations using specific markers we have identified in this 482

study. Zhu and colleagues [43] have shown that bone marrow-derived monocytes make up 483

approximately 80% of MHC-II–positive macrophages in a PDA GEMM whereas MHC-II–484

negative macrophages in normal pancreas and PDA were shown to be maintained 485

independently of monocyte contributions. Monocyte-independent MHC-IIlow tissue resident 486

macrophages expanded during tumor progression and contributed to PDA growth and survival. 487

Conversely, Sanford and colleagues [44] have shown that monocytes can give rise to a pro-488

inflammatory macrophage population in a PDA mouse model, which when antagonized with 489

neutralizing antibodies against CCR2, resulted in decreased tumor growth and reduced 490

metastases in vivo [44]. These data highlight the need for an scRNA-seq study on macrophage 491

populations in PDA GEMMs with labelled bone marrow replacement to reconcile these 492

discrepancies. 493

494

More importantly, in the studies of tumor-associated macrophages, inflammatory chemokines 495

are commonly used to indicate an M1 type of macrophage, which are normally associated with 496

immune-stimulatory functions. Nevertheless, our study indicates that a distinct M1/M2 497

macrophage phenotype is not readily discernable at the single-cell level. Instead, as PDA 498


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16

progresses, an inflammatory feature is substantially increased, and this accompanies an 499

increase of an important M2 macrophage marker, ARG1 (Fig. 4F and H). This raises questions 500

on the M1/M2 classification system, as the inflammatory feature is associated with the 501

progression of PDA. Future studies should focus on the function of these inflammatory 502

macrophages in PDA in addition to validating markers for macrophage classification. 503

504

While numerous studies have generally shown that CAFs are tumor-promoting in the biology of 505

PDA and other carcinomas [45, 46], recent studies have found that the function of CAFs in PDA 506

biology are more varied. Özdemir and colleagues [42] demonstrated that the depletion of 507

αSMA+ cells from the microenvironment in a PDA GEMM resulted in shortened survival and 508

poorly differentiated tumors [42], and low myofibroblast tumor content was shown to be 509

associated with worse survival in human PDA sections. These data prompted a paradigm shift 510

whereby certain CAFs may function to constrain, rather than promote, PDA. Moreover, until 511

recently, the molecular heterogeneity of CAFs in PDA has not been well-appreciated. The 512

primary attempt to characterize fibroblast heterogeneity in PDA demonstrated that mouse 513

pancreatic stellate cells (PSCs) could be induced to express αSMA in vitro when directly co-514

cultured with primary mouse PDA cells in an organoid co-culture system [22]. These 515

myofibroblastic CAFs were designated as “myCAFs.” This was distinct from IL6+ fibroblasts that 516

were produced in vitro when PSCs were indirectly co-cultured with mouse PDA organoids 517

through a semi-permeable membrane. The IL6+ fibroblasts were also positive for PDGFRα and 518

numerous other cytokines and therefore termed inflammatory CAFs or “iCAFs.” The 519

immunohistochemistry of human and mouse PDA tissue showed distal IL6+ stroma as a distinct 520

population from the peritumoral αSMA+ stroma. Subsequent studies in PDA GEMMs 521

demonstrated that the iCAF population can mediate pro-tumorigenic properties and is a 522

potential therapeutic target in an attempt to sensitize PDA to immunotherapeutic strategies [23, 523

24]. 524

525

Our current study is the first to demonstrate the existence of three distinct molecular subtypes of 526

fibroblasts in the normal mouse pancreas, which in turn gave rise to two distinct subtypes of 527

CAFs that were largely conserved across three different PDA GEMMs. We noted that FB1 528

expressed insulin-like growth factor signaling genes (Igfbp7, Igfbp4, and Igf1) in addition to 529

Pdgfra, Cxcl12, Il6, and several other cytokines (Ccl11, Ccl7, Ccl2, and Csf1). We propose that 530

our FB1 population is the previously described iCAF population and hence likely pro-531

tumorigenic. Conversely, the FB3 population was positive for the myofibroblast markers Acta2 532


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17

and Tagln, and therefore most closely represents the previously described myCAF population. 533

Importantly, our agnostic approach did not identify any further putative CAF populations and so 534

we support the two-CAF model proposed by Öhlund and colleagues [22]. 535

536

In summary, this report systematically outlines the cellular landscape during the progression of 537

PDA and highlights the cellular heterogeneity in PDA pathogenesis. As such, future targeted 538

therapeutic strategies should be developed with their intended target subpopulation in mind. 539

540

Address requests for reprints to: Rolf A Brekken, 6000 Harry Hines Blvd., Dallas, TX 75390-541

8593. Email: [email protected]. 542

543

Acknowledgements 544

We thank the McDermott Center Next-Generation Sequencing Core at UT Southwestern for 545

preparing and sequencing the single-cell RNA-seq libraries. We thank Dr Jeon Lee (UT 546

Southwestern) for assistance in pre-processing of scRNA-seq data. We also thank Dave Primm 547

of the UT Southwestern Department of Surgery for help in editing this article. 548

549

Conflicts of interest 550

The authors have no conflicts of interest to report. 551

552

Ethics approval 553

Mice were used in this study according to UT Southwestern institutional guidelines and 554

approved by the institutional animal care and use committee at UT Southwestern Medical 555

Center. All human samples were procured through the UT Southwestern tissue management 556

shared resource and approved through the UT Southwestern institutional review board. 557

558

Data sharing statement 559

There are no additional unpublished data from this study. 560

561

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662


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663


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Figure Legends 664

665

Figure 1. Cellular heterogeneity during PDA progression. A) Representative H&E sections 666

of the normal pancreas, early KIC lesion, and late KIC lesion (magnification: 20x). B) tSNE plot 667

of normal pancreas displaying 2354 cells comprising 8 distinct cell populations. C) tSNE plot of 668

the early KIC lesion displaying 3524 cells containing 9 cells types with the emergence of the 669

cancer cell population. D) tSNE plot of the late KIC tumor showing 804 cells and 7 distinct 670

populations. Stacked violin plots of representative marker gene expression for each of the cell 671

populations seen in the E) normal pancreas, F) early KIC lesions, and G) late KIC lesions. 672

673

Figure 2. Analysis of early and late KIC cancer cell populations demonstrate the 674

emergence of the mesenchymal cancer cell population as a late event. A) tSNE plots of the 675

early KIC lesion demonstrated the expression of known epithelial markers in the sole cancer 676

population (black outline). Mesenchymal markers were absent in this population. B) tSNE plots 677

demonstrating the emergence of two cancer cell populations in the late KIC tumor. One cancer 678

cell population expressed the epithelial markers (smaller population outlined in black) and a 679

second expressed the mesenchymal markers (larger population outlined in black). C) Violin 680

plots showing the high expression of epithelial markers in the early KIC cancer cell population 681

and late KIC epithelial cancer cell population but not in the mesenchymal population. 682

Mesenchymal markers were overexpressed in the mesenchymal cancer cell population but not 683

in the early KIC or late KIC epithelial cancer cell populations. D) Single-cell profiling heatmap of 684

all early and late KIC cancer cells displaying differentially expressed genes between the three 685

cell populations. Gene names are listed in the boxes on the far right of the heatmap. Each 686

column represents an individual cell and each row is the gene expression value for a single 687

gene. E) KEGG pathway analysis and F) gene ontology analysis comparing all early KIC cancer 688

cells against all late KIC cancer cells. Red bars are increased categories and blue bars are 689

decreased categories. G) KEGG and BIOCARTA pathway analysis and H) gene ontology 690

analysis comparing all early KIC cancer cells against only the late KIC epithelial cells. Red bars 691

are categories increased in the late KIC and blue bars are decreased in the late KIC. (****P < 692

0.0001, ***P < 0.001, **P < 0.01, *P < 0.05). 693

694

Figure 3. Comparison between cancer cells of KIC and KPfC tumors. A) tSNE plot of the 695

late KPfC lesion displaying 2893 cells and 8 distinct cell populations. B) Stacked violin plots 696

showing representative marker gene expression for each of the cell populations seen in the late 697


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22

KPfC lesion. C) Single-gene tSNE plots of the KPfC tumor displaying the presence of epithelial 698

markers (Ocln, Gjb1, and Tjp1) in the epithelial cancer cell population (upper black outlined 699

population) and mesenchymal markers in the mesenchymal cancer cell population (lower black 700

outlined population). D) Violin plots showing the overexpression of epithelial markers in the 701

epithelial cancer cell population and mesenchymal markers in the mesenchymal cancer cell 702

population. E) KEGG and BIOCARTA pathway analysis comparing all late KIC to all late KPfC 703

cancer cells. Red bars are categories increased in the late KIC and blue bars are increased in 704

KPfC. F) Single-cell profiling heatmap comparing all cancer cells in the KIC versus all cancer 705

cells in the KPfC. Each column represents an individual cell and each row is the gene 706

expression value for a single gene. (****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05). 707

708

Figure 4. scRNA-seq analysis of KIC tumor progression reveals multiple subpopulations 709

of macrophages. A) tSNE plot of three macrophage subpopulations in the early KIC tumor. B) 710

Heatmap depicting the 30 top significantly overexpressed genes in each of the three early KIC 711

macrophage subpopulations. Macrophages from the normal pancreas are displayed (far left 712

group). Each column represents an individual cell and each row is the gene expression value for 713

a single gene. C) tSNE plot representation of two macrophage subpopulations in the late KIC. 714

D) Heatmap depicting the 30 top significantly overexpressed genes in each of the two late KIC 715

macrophage subpopulations. Each column represents an individual cell and each row is the 716

gene expression value for a single gene. E) GO analysis of biological processes in the three 717

macrophage subpopulations seen in the early KIC. F) GO analysis of biological processes in the 718

two macrophage subpopulations of the late KIC. G) Violin plots of the expression of 719

inflammatory genes and Arg1 comparing the macrophages in early and late KIC. H) GO 720

analysis of biological processes that are upregulated in the late KIC macrophages relative to 721

early KIC macrophages. (****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05). 722

723

Figure 5. Analysis of fibroblasts during PDA progression reveals multiple molecular 724

subtypes. A) All fibroblasts from the normal pancreas, early KIC tumors, and late KIC tumors 725

were projected onto a single tSNE plot with the FB1, FB2, and FB3 populations distinguished by 726

pink, orange, and brown, respectively (upper left panel). Normal pancreas fibroblasts were 727

highlighted in red (upper right panel), early KIC fibroblasts in green (lower left panel) and late 728

KIC fibroblasts in blue (lower right panel). Normal pancreas and early KIC contained fibroblasts 729

in all three groups whereas the late KIC had only FB1 and FB3. B) Heatmap displaying the top 730

significant genes (cutoff: P < 10-40) for each of the three fibroblast populations. Thirty random 731


https://doi.org/10.1101/539874

23

cells from each fibroblast population are displayed. All three late-cancer GEMMs (late KIC, 732

KPfC, and KPC) display only FB1 and FB3 fibroblast populations. C) Violin plots demonstrating 733

representative marker genes for each fibroblast subtype: FB1 overexpressed cytokines and 734

Pdgfra. FB3 overexpressed mesothelial markers, myofibroblast markers, MHC-II molecules and 735

Cdh11. D) Gene ontology analysis of the top biological processes in each of the three fibroblast 736

subtypes. E) GO analysis of genes upregulated in late FB1 and FB3 compared to early FB1 and 737

FB3 in KIC, respectively. F) Immunohistochemical analysis of PDGFRα, αSMA, and CDH11 738

stained serially on the same slide. Colors were deconvoluted into a single color layer. PDGFRα 739

and αSMA staining were mutually exclusive, whereas CDH11 was a pan-CAF marker 740

(magnification: 20x). (****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05). 741

742

Figure 6. Analysis of transcriptional activity in different stages of PDA reveals differential 743

epigenetic and super-enhancer activity in distinct tissue compartments. A) tSNE plot of 744

UMI counts in the early and B) late KIC. C) Violin plots of epigenetic regulatory genes in the 745

cancer cell populations of normal pancreas, early KIC, and late KIC. D) Violin plots of epigenetic 746

regulator genes in the normal, early fibroblast, and late fibroblast populations showing their 747

upregulation in CAFs. E) Sequential triple immunohistochemical staining on the same late KIC 748

tumor section for cancer cells (SOX9, pink), mesenchymal cells (vimentin, brown) and super-749

enhancer activity (BRD4, blue). Well-differentiated ductal epithelium stained solely for SOX9 750

(green outline). Mesenchymal cancer cells (blue arrows) and CAFs (brown arrows) both show 751

co-staining with BRD4. F) Immunohistochemical analysis of human PDA whole tissue sections 752

using the H3K27ac antibody. These representative figures from four different human PDAs 753

demonstrate the 3+/3+ staining in the stromal fibroblasts (red arrows) with 1-2+ staining in the 754

cancer epithelium (magnification: 20x). 755

756

757


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24

Supplementary Figure 1. Ultrasound image of early KIC mouse (39 days old), 1 day prior to 758

sacrifice. Two-dimensional measurement of the neoplastic lesion is denoted in teal (1.10 mm x 759

0.82 mm). V = ventral surface, D = dorsal surface, S = spleen. 760

761

Supplementary Figure 2. A) tSNE plot of KPC (6 months) displaying 1007 cells making up 8 762

distinct cell populations as indicated. B) Stacked violin plots of marker genes and UMI for each 763

of the 8 KPC populations. 764

765

Supplementary Figure 3. Heatmap depicting gene expression levels (horizontal) of single cells 766

(vertical) in the macrophage populations of the KPfC GEMMs. Proinflammatory (Macrophage 1) 767

and MHC-II–rich (Macrophage 2) subtypes are noted below the heatmap. 768

769

Supplementary Figure 4. A) The relative proportions of FB1, FB2, and FB3 in the normal 770

mouse pancreas, early KIC lesions, and late KIC lesions. B) Genes increased most significantly 771

in the FB1 and FB3 populations as the normal pancreas progressed to early KIC and then to 772

late KIC. 773

774

Supplementary Figure 5. Violin plots depicting gene expression of Myc and epigenetic 775

regulatory genes in the KPfC epithelial and mesenchymal cancer cell populations. 776

777

778


https://doi.org/10.1101/539874

Normal Pancreas Early PDA Late PDAH

&E

Fibroblasts-2

10.11%

Fibroblasts-1

6.29%

Islet cells

1.15%

T cells

10.07%

B cells

5.52%

Fibroblasts-3 1.36%

Acinar cells 61.68%

Macrophages

3.82%

Fibroblasts-2

26.65%

Lymphocytes

7.71%

Red blood

cells 7.59%

Cancer cells-1

3.73%

Cancer cells-2

27.86%

Macrophages 42.41%

Fibroblasts-1 4.98%

Fibroblasts-3

5.72%

A

B C Dnormal pancreas 2354 cells early KIC 3524 cells late KIC 804 cells

Islet cells

3.83%

Macrophages

27.70%

Acinar cells

13.22%

Endothelial cells

5.16% Fibroblasts-3 1.02%

Fibroblasts-1 11.01%

Cancer cells 9.90%

Beta cells 1.50%

Fibroblasts-2

26.65%

Amy1

Amy2a2

Pyy

Sst

Ins1

Ins2

Sox9

Krt18

Col1a1

Col1a2

Cdh5

Pecam1

Cd14

Cd52

Sox9

Krt18

Col1a1

Col1a2

Cd14

Adgre1

Cd3d

Cd79b

Hba-a1

Hba-a2

E F GAmy1

Amy2a2

Col1a1

Col1a2

Pyy

Sst

Cd3d

Cd3e

Cd19

Cd79b

Cd14

Adgre1

Figure 1


https://doi.org/10.1101/539874

KIC Cancer Cells

Fold Enrichment

KIC Cancer Cell Progression

KEGG Pathway Analysis

Top Down-regulated

Top Up-regulated

KIC Cancer Cell Progression

Gene Ontology Analysis

Fold Enrichment

Bio

log

ica

l Pro

ce

ss

Ce

llula

r Co

mp

on

en

t

Top Down-regulated

Top Up-regulated

Cancer cells

(epithelial)

Earl

y K

IC

Cancer cells-1

(epithelial)

Cancer cells-2

(mesenchymal)

Cdh1 Epcam Gjb1 Cdh1 Epcam Gjb1

Cdh2 Cd44 Axl Cdh2 Cd44 AxlLa

te K

IC

A B

C D

E F

Cdh1

Aplp2

Sepp1

Gcg

Wfdc18

Zbtb20

Prox1

Cp

Cldn3

Tm4sf4

Ttr

Dcdc2a

Apoe

Ces1d

Mgst1

Ambp

Spp1

Spink1

Cel

Klk1

Zg16

Reg2

Iapp

Gstm1

Ins1

Ctrl

Rnase1

Reg1

Try5

Cela1

Ctrb1

Clps

Prss2

Cela2a

Cela3b

Sycn

Ins2

Cpb1

Try4

Cpa1

Pnliprp1

Pnlip

Cldn10

Wfdc15b

Tmem176a

Tmem176b

Tmem45a

2210010C04Rik

Late Epithelial Cancer Cells

Early Cancer CellsLate Mesenchymal

Cancer Cells Ggct

Ctgf

Meg3

Serpine1

Inhba

Igfbp3

Npy

Areg

Lgals1

Fxyd5

S100a4

Ctla2a

Sox11

Igfbp4

F2r

Axl

Hmga2

Prkg2

Timp1

Ecm1

Cpe

Emp1

Cd44

Plaur

Pmepa1

Sdc1

Vim

Ldha

Pgk1

Lgals3

Pkm

Anxa1

Nbl1

Anxa2

S100a10

Id1

Plac8

Msln

Rbp1

S100a6

Tspan8

Onecut2

Anxa3

2200002D01Rik

Gsto1

S100a11

Tmsb4x

Rpl29

Tpt1

Rbm3

Basp1

Mcpt2

Cldn2

Ly6e

Epcam

Cldn7

Krtcap3

Cldn4

Wfdc2

Tff2

Krt7

Sfn

Krt20

Ly6a

S100a14

Serpinb1a

Krt19

Pglyrp1

Fxyd3

Muc1

Dmbt1

Gsta4

Lgals4

Tff1

Ly6d

Mmp7

Slpi

2210407C18Rik

Gcnt3

Spint2

Aqp5

Mal2

Anxa5

Ifitm3

Sparc

relative expressionlow high

Fold Enrichment

Cellu

lar C

om

po

ne

nt

Bio

log

ica

l Pro

ce

ss

Early and Late KIC

Epithelial Cancer Cell Comparison

Gene Ontology Analysis

Top Down-regulated

Top Up-regulated

Fold Enrichment

BIO

CA

RT

A

KE

GG

Top Down-regulated

Top Up-regulated

Early and Late KIC

Epithelial Cancer Cell Comparison

Pathway AnalysisGH

Figure 2

Cdh1

Epcam

Cldn3

Cdh2

Vim

S100a4


https://doi.org/10.1101/539874

Cancer cells-1

13.07%

Cancer cells-2

5.22%

Lymphocytes 2.45%

Macrophages

65.64%Granulocytes

3.94%

Fibroblasts-3

1.76% Red blood cells 3.49%

Fibroblasts-1 4.42%

late KPfC 2893 cells

Top Down-regulated in KIC

Top Up-regulated in KIC

KE

GG

B

IOC

AR

TA

Pathway Comparison between

Cancer Cells in Late KIC and Late KPfC

Fold Enrichment

Gm10260

Gm10709

Rps27rt

Gm8797

Mt1

Mt2Rps18−ps3

Gm8730

Ggct

Nrn1

Igfbp3

Meg3

Igfbp7

Itgb1

Tuba1a

Timp1

Cpe

Hmga2

Lgals1

Vim

Igfbp4

Msn

Fbln2

F2r

Axl

Prkg2

Prkg2

Serpine1

Inhba

Id3

S100a4

Fxyd5

Ctla2a

Mcpt8

Ctgf

Ankrd1

Tnc

Npy Prl2c3 Lgals3 Ldha Pgk1

Cdkn2a Cldn6 Lars2 Mcpt1 Mcpt2

AA467197

Uba52

Wfdc17

Lyz2Ccl6Saa3

Cxcl2Cd74

mt-Co1

Phgr1

Prap1

Gkn1

Tff1

Reg1

Reg3b

Reg3g

Agr2

Gkn3

Gpx2

Ltf

Prom1

Pglyrp1

Fxyd3

Gsta4

Lgals4

Muc1

Car2

Dmbt1

Tff2

Pdzk1ip1

Sftpd

Sepp1Aqp5

Cldn4

Sfn

Cldn3

S100a14

Ly6a

Ly6e

Wfdc2

Epcam

Cldn7

Spint2

Krt7

Krt19

Cdh1

Krtcap3

Mal2

2210407C18Rik

Mmp7

Rbp1

Serpinb1a

Apoe Cp Wfdc18 Ttr Tmem45a

Epithelial Cancer Cells

Mesenchymal

Cancer Cells

Epithelial

Cancer Cells

Mesenchymal

Cancer Cells

KIC KPfC


La

te K

PfC

Cancer cells-1

(epithelial)

Cancer cells-2

(mesenchymal)

Ocln Gjb1

Vim Cd44

Tjp1

Axl

KPfC Cancer Cells

Gjb1

Tjp1

Cldn3

Vim

S100a4

Fbln2

Sox9

Krt18

Col1a1

Col1a2

Cd3d

Cd79

Hba-a1

Hba-a2

Adgre1

Fcgr2b

Itgax

S100a9

A

B

C

D

E

F

Figure 3


https://doi.org/10.1101/539874

Macrophage1

Macrophage2

Late KIC

Macrophage1

Macrophage2

Macrophage3

Early KIC

Early KIC Macrophage Signatures

Gene Ontology Biological Process Analysis

Ma

cro

ph

ag

e 1

Ma

cro

ph

ag

e 2

Ma

cro

ph

ag

e 3

Fold Enrichment

Late KIC Macrophage Signatures


Ma

cro

ph

ag

e 2

Ma

cro

ph

ag

e 1

Fold Enrichment

Figure 4

C

E

F H

Fold Enrichment

Early and Late KIC Macrophages Comparison


Top Up-regulated

G

A B

D

low

relative expression

high

Late KIC

Macrophage1

Late KIC

Macrophage2 Anxa1

Emp3

Cd44

Sdc4

Itgam

S100a6

Arg1

Wfdc21

Cxcl3

Ier3

F13a1

Ear10

Osm

Ifitm6

Pf4

Chil3

Lrg1

Ccl6

Ccl9

Wfdc17

Slpi

S100a8

Ccl7

Hbb−bs

Lcn2

Ear1

Ccl2

Ear2

Thbs1

Saa3

C1qb

Aif1

Il23a

H2−Aa

H2−Eb1

H2−Ab1

H2−Dma

H2−DMb1

H2−DMb2

Rgs1

Tspan13

Cst3

C1qa

Acp5

Spp1

Plet1

C1qc

Mgl2

Cxcl16

Mmp12

Mmp13

Cd74

AW112010

Adamdec1

Ccr7

Ifitm1

Ccl17

Tbc1d4

Apoe

Il1r2

Arg1

Il1a

Il1b

Il1r2

Il6

KIC Macrophages

Early KIC

Macrophage3

Early KIC

Macrophage1

Normal Pancreas

Macrophage

Early KIC

Macrophage2


Sod2

H2−DMb1

Il1a

Ctsz

Prdx5

Bcl2a1b

Capg

Ccrl2

Fabp5

Timp1

Rnf149

Il1rn

Trem2

Il1b

Tgm2

Tnfaip2

Ctsd

Cd14

Ctss

Thbs1

Lgals3

Spp1

Lyz2

Slc7a11

Clec4e

Ptgs2

Ear1

Ear2

Lyz1

Fn1

Timp2

Ifi27l2a

Emp1

Glul

Ednrb

Cfh

Hbegf

Pf4

Ltc4s

Pltp

Mrc1

C4b

Wfdc17

Hmox1

Ccl4

Ccl8

Gas6

Fcgrt

Sepp1

Cd163

Lyve1

Cd209g

Cbr2

Ccl2

Ccl12

Folr2

F13a1

Ccl7

Cd209f

Fcna

Etv3

Gm2a

H2afy

Rpl4

Gpr132

Crem

Ccnd2

Vps37b

Jak2

Tnpo3

Tmsb10

Tmem123

Mkrn1

Psmb8

Syngr2

Lsp1

Pkib

Sub1

Cytip

Plbd1

Cst3

Ramp3

Plac8

Tspan13

Il1r2

Tbc1d4

Napsa

Ccr7

Ccl17

H2afz


https://doi.org/10.1101/539874

FB3

FB3

FB1

FB3

FB2

FB1

FB2

FB1

FB2

FB1

FB3

FB2

Fibroblast Clusters Normal Pancreas

Early KIC Late KIC

FB

1F

B3

Fold Enrichment

Gene Ontology Analysis of KIC Fibroblast

Increasing Signature

Fold Enrichment

PDA Fibroblast Signatures


FB

1F

B2

FB

3

Ccl7

Ccl2

Cxcl12

Pdgfra

Il6

Msln

Lrrn4

Upk3b

Cd74

H2-Aa

H2-Dmb1

Acta2

Tagln

Cdh11

Late KIC

Cadherin-11/PDGFRα/αSMA Cadherin-11

PDGFRα αSMA

Ifitm2

Mt1

Serpinh1

Junb

Mgp

Il6

Ccl7

Ccl11

Cxcl2

FB1 FB2 FB3low high

relative expression

Apoe

Thbs1

Bgn

Cygb

Cxcl14

Pcolce

Malat1

Gpx3

Itm2a

Ptn

Sfrp1

Smoc2

Jund

Abca8a

Steap4

Srpx

Fosb

Igf1

Top genes

in the heatmap

Other genes

in the clusters

Jun

Egr1

Gdf10

Gpm6b

Ifitm1

Cxcl1

Cyp1b1

G0s2

Emp1

Pi16

Sema3c

Ly6c1

Timp2

Clec3b

Pcolce2

Efemp1

Creb5

Nov

Cd248

Slco3a1

Rpl35a

Gpc6

Scara3

Serpinb6a

Rnase4

Rpl17

Gstm1

Ptgs1

Lrrn4

Gpm6a

Nkain4

Msln

Lgals7

Krt19

Upk1b

Slpi

Bcam

Myrf

Bst1

Cav1

F11r

Arhgdib

H2-DMb1

Rpp25

Hdc

Plxna4

Cd38

Clu

Cd82

Cmbl

C2

Ndufaf1

Csrp2

Spint2

Stmn2

Cdh11

Cxadr

Cd74

C1rb

C1d

C3

Tpm1

Anxa11

Mdh2

Mdk

Lpcat3

H2-Aa

H2-Ab1

H2-Eb1

H2-DMb1

H2-D1

Cyb5r3

Pkm

Atp9a

Mgat4b

Ltbp3

Ces2g

Hist1h1c

Dkk3

Cbx5

Sfxn3

Nxn

Daglb

FB1

FB2

FB3

BA

D

C

E

F

Figure 5

FB3

FB3


https://doi.org/10.1101/539874

Lymphocytes

Red blood

cells

Cancer cells-1

(epithelial)

Cancer cells-2

(mesenchymal)

Macrophages

Fibroblasts-1

Fibroblasts-3

Late KIC nUMI

Islet cells

Macrophages

Acinar cells

Endothelial cells Fibroblasts-3

Fibroblasts-1

Cancer cells

Beta cells

Fibroblasts-2

Early KIC nUMI

nUMI

Brd4

Myc

Arid1a

Arid2

Smarcb1

Hmga1

Hmga1-rs

Hmga2

nUMI

Brd4

Arid1a

Smarcb1

A B

C D

E

Figure 6

Late KIC

SO

X9

/Vim

en

tin

/BR

D4

FibroblastsMesenchymal Cancer CellsEpithelial Cancer Cells

F

Hu

ma

n P

DA

H3K

27

ac


https://doi.org/10.1101/539874

Supplementary Figure 1

S

V

D


https://doi.org/10.1101/539874

A

B


Sox9

Krt18

Col1a1

Col1a2

Cd3d

Cd79a

Peacam

Cdh5

Hba-a1

Cd14

Fcgr2b

Fibroblasts-3

4.37%

Fibroblasts-1

5.26%

Macrophages

64.45%

B lymphocytes

9.33%

Red blood

cells 2.48%

T lymphocytes 9.24%

Cancer cells 2.88%

Endothelial cells 1.99%

late KPC 1007 cells


https://doi.org/10.1101/539874


Macrophage 1 Macrophage 2

KPfC Macrophages


https://doi.org/10.1101/539874

0%

20%

40%

60%

80%

100%

NormalPancreas

Early KIC Late KIC

FB1

FB2

FB3

FB1

FB2

FB3

FB1

FB3

35.41%

56.94%

7.66%

28.47%

68.89%

2.64%

46.51%

53.49%

KIC Fibroblast Populations

during PDA Progression

Normal

Pancreas

Late KIC

KIC FB1 Increasing Signature

Early KIC

Normal

Pancreas

KIC FB3 Increasing Signature

Late KIC

Early KIC


A

B


https://doi.org/10.1101/539874


Myc

Brd4

Hgma1

Hgma2


https://doi.org/10.1101/539874

cellular heterogeneity during mouse pancreatic …gel bead in emulsion (gem) was incubated and then...

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